Optimizing the performance of a semiconductor handler means tuning its mechanical motions so that the semiconductor handler operates at maximum throughput with the slowest mechanical motions for a given test time.
In semiconductor manufacture, semiconductor device test equipment is a costly capital requirement. Conventionally, such test equipment has included a robotic manipulator for handling the devices being tested. This robotic manipulator is commonly referred to as a “handler” and is typically configured with one or more robotic arms referred to as “manipulators.” The manipulator mechanically picks up a device for testing, inserts the device into an interface test board and issues a start-of-test signal to the tester. The tester then conducts a test on the device and returns a test result and an end-of-test signal to the handler that causes the handler to disposition the device to a post-test tray or receptacle for holding tested devices. This process is repeated as long as the handler senses that there are additional devices available for test. This system as a whole is sometimes referred to as a “test cell.”
During the time required for the handler to disposition a device(s) just tested and replace the device(s) with the next device(s) to be tested, the tester remains substantially idle. This idle time is sometimes referred to as “index time” for the particular tester and system, and involves mechanical manipulations of the devices awaiting test and having been tested. These mechanical manipulations are limited in speed of operations by various factors, including, for example, physical and speed constraints to ensure that devices to be tested are not damaged, contaminated, dropped, and the like.
The time required to test a device is sometimes referred to as “test time” for a particular device, test, tester, and system. When the system is operational in a manufacturing capacity, it is either indexing during the index time or otherwise testing during the test time.
Previously, test equipment manufacturers have focused efforts to reduce index time on design of manufacturing equipment to increase speed of mechanical operations. Although speeds of mechanical operations in handling the test devices have increased significantly over time, there nonetheless remains significant mechanical index time required to manipulate test devices between tests, by the robotic handlers. Moreover, with increased speeds of mechanical manipulation equipment operations, costs increase for the equipment, including calibration, replacement frequency, maintenance, parts, and others. Given the constraints and precautions that must be addressed in speeding mechanical manipulations of many types of test devices and handlers, further speeding of mechanical operations is subject to economic and physical barriers.
In any event, reducing index time can provide greater returns on investments in test equipment, particularly where the test equipment is costly. It would therefore be a significant improvement in the art and the technology to further reduce index time involved in test operations in manufacturing environments. Particularly in semiconductor manufacture, economic and other gains and advantages are possible if index times are reduced in the testing of semiconductor equipment. It would also be an improvement to provide new and improved systems and methods for achieving reduced index times, without requiring substantial changes or new developments in existing mechanical operations of device handlers and similar robotic or automated components for the testing. Examples of recent advancements made by the present inventor in reducing index time for automated and robotic semiconductor testing are disclosed in other patents by the present inventor, including U.S. Pat. No. 7,183,785 B2, U.S. Pat. No. 7,508,191 B2, U.S. Pat. No. 7,619,432 B2, and U.S. Pat. No. 8,400,180 B2.
In addition to the advantages of reducing index times discussed above, it would also be an improvement to provide new and improved systems and methods for setting up and configuring the control systems for automated and robotic semiconductor test equipment, which further reduces the cost, complexity, index time, and downtime of testing operations associated with the automated and robotic semiconductor test equipment. For instance, conventional automated semiconductor test equipment is commonly serialized, where each individual test is performed sequentially. The primary reasons for serialized testing include thermal issues that restrict the number and complexity of tests that can be conducted at one time, and the complexity of implementation for non-serialized testing. Additionally, conventional Design for Test (DFT) testing normally requires exclusive control of the testing device when running, thus preventing any other non-DFT test from being executed at the same time.
This application applies to handlers in general, but has particular significance for handler technology wherein two or more handlers are multiplexed together to a single tester, or wherein two or more multiplexed handlers are integrated into a single operational unit and connected to a single tester. These systems are referred to as indexless test cells. With indexless systems, the key feature is that there are two or more asynchronous but coordinated core manipulators that can have overlapping insertions for device test. Testing is still performed sequentially, but alternates between the handlers or manipulators. The multiple test sites where these manipulators insert devices for test are electronically multiplexed to the tester. In this way, the elapsed time between tests is minimized.
The benefit of handler optimization is that throughput of the handler is maximized while stress on the handler is minimized. When applied across an entire test floor, this translates to higher throughput per unit area of factory floor space and lower cost of test for the enterprise.
The specific case of an indexless handler transport system presents opportunities for handler optimization that are not present in conventional handler designs. For example, in the case where test time is longer than index time, there is always an opportunity to increase index time without penalty. Whereas with standard handlers that are not input/output (I/O) limited (running at maximum throughput), increasing index time will always result in a reduction of throughput (a penalty) unless there is a secondary result that is sufficiently compensating, such as a lowering of the jam-rate.
The handler motions that are tuned may include, but are not limited to: velocity, acceleration, jerk, path, and sequence. One reason for tuning a robotic handler is to reduce stress on mechanical and electrical components and to thereby reduce the frequency of handler malfunctions and failures. The operational benefit is lower operating overhead. The financial benefit is lower cost of goods sold and higher profit margins.
Many robotic device handlers allow an operator or user to manually adjust the mechanical motions of a semiconductor handler by making changes and adjustments via a software user-interface or control panel. The factors that determine these mechanical motion settings vary from company to company, but often can become a source of controversy, argument, and contention. This contributes to friction between the manufacturing centers that operate the equipment and other company divisions that design, manage, and sell the semiconductor products being tested by the ATE assets. This translates to unnecessary organizational inefficiency. The primary reason for the friction is that there are no clear data for what settings result in the lowest cost of test for the product mix being run on a given handler. Typically, the business centers see the solution as tuning the handlers for the maximum throughput supported by the handler while the manufacturing centers know that the optimal operational speed is slower than the maximum capability of the handler. This optimal point is where a complex set of trade-offs comes into balance and results, over the long term, in the lowest cost of test. These trade-offs include considerations that increase mechanical motions and those that decrease mechanical motions.
The single prime example of the motivation to increase mechanical motions is higher throughput (raw output capacity). Unfortunately, running a handler at maximum speed causes opposing factors to increase in magnitude and frequency. The net effect is to decrease the long-term throughput of the system. A simple example would be the consequence of running a handler so fast that a critical mechanical component fails under the high stresses associated with high-speed operation. When this happens, the operation is halted. Depending upon the mean-time-to-repair, the down time could significantly degrade the long-term throughput to such an extent that the alternative strategy of running more slowly results in higher long-term throughput.
Examples of motivations to slow mechanical motions are those conditions and events that require the handler to be interrupted or halted for long periods of time. These include handler malfunctions, jams, mishandled devices, mean-time-between-failure (MTBF), mean-time-between-assists (MTBA), frequency of required calibrations, and frequency of scheduled maintenance. A simple example of how these factors work against long-term throughput can be made by analogy. Competitive auto racing deals with similar trade-offs in determining a racing strategy. Run the car faster and you may blow an engine or damage a critical component and drop out of competition. However, if you can find the perfect balance between racing speed and stress on the vehicle, the slower racing speeds may put you across the finish line first because the car completed the course without having to stop as often for repairs and adjustments. Essentially, it is a classic example of trading faster instantaneous velocity in exchange for faster speed (average velocity). Ultimately, the team is looking for the optimal point where a complex set of trade-offs comes into balance and results, over the long term, in the fastest speed.
In describing the methodologies used in tuning a semiconductor handler, the Theory of Constraints is employed. Another common term for the Theory of Constraints is the “Theory of Bottlenecks.” The Theory of Constraints will be referred to as “ToC.” Wikipedia describes ToC as follows: The theory of constraints (ToC) is a management paradigm that views any manageable system as being limited in achieving more of its goals by a very small number of constraints. There is always at least one constraint, and ToC uses a focusing process to identify the constraint, and to restructure the rest of the organization around it. ToC adopts the common idiom “a chain is no stronger than its weakest link.” This means that processes, organizations, etc., are vulnerable because the weakest person or part can always damage or break them or at least adversely affect the outcome.
The underlying principles of ToC as they are applied to the optimization of handler performance are described later in this application. However, for the sake of context, some fundamental elements to be leveraged are included below (Key Assumptions, The Five Focusing Steps, and Application) as Wikipedia describes them:
Key Assumptions:
The underlying premise of theory of constraints is that organizations can be measured and controlled by variations on three measures: throughput, operational expense, and inventory. Throughput is the rate at which the system generates money through sales. Inventory is all the money that the system has invested in purchasing things which it intends to sell. Operational expense is all the money the system spends in order to turn inventory into throughput. Before the goal itself can be reached, necessary conditions must first be met. These typically include safety, quality, legal obligations, etc. For most businesses, the goal itself is to make money. However, for many organizations and non-profit businesses, making money is a necessary condition for pursuing the goal. Whether it is the goal or a necessary condition, understanding how to make sound financial decisions based on throughput, inventory, and operating expense is a critical requirement.
The Five Focusing Steps:
Theory of constraints is based on the premise that the rate of goal achievement by a goal-oriented system (i.e., the system's throughput) is limited by at least one constraint. The argument by reduction ad absurdum is as follows: If there was nothing preventing a system from achieving higher throughput (i.e., more goal units in a unit of time), its throughput would be infinite—which is impossible in a real-life system. Only by increasing flow through the constraint can overall throughput be increased. Assuming the goal of a system has been articulated and its measurements defined, the steps are:                (1) Identify the system's constraint(s) (that which prevents the organization from obtaining more of the goal in a unit of time.        (2) Decide how to exploit the system's constraint(s) (how to get the most out of the constraint).        (3) Subordinate everything else to the above decision (align the whole system or organization to support the decision made above).        (4) Elevate the system's constraint(s) (make other major changes needed to increase the constraint's capacity).        (5) Warning! If in the previous steps a constraint has been broken, go back to step 1, but do not allow inertia to cause a system's constraint.The goal of a commercial organization is: “Make more money now and in the future”, and its measurements are given by throughput accounting as: throughput, inventory, and operating expenses. The five focusing steps aim to ensure ongoing improvement efforts are centered on the organization's constraint(s). In the TOC literature, this is referred to as the process of ongoing improvement (POOGI). These focusing steps are the key steps to developing the specific applications mentioned below.        
Within manufacturing operations and operations management, the solution seeks to pull materials through the system, rather than push them into the system. The primary methodology use is drum-buffer-rope (DBR) and a variation called simplified drum-buffer-rope (S-DBR). Drum-buffer-rope is a manufacturing execution methodology, named for its three components. The drum is the physical constraint of the plant: the work center or machine or operation that limits the ability of the entire system to produce more. The rest of the plant follows the beat of the drum. They make sure the drum has work and that anything the drum has processed does not get wasted.
The buffer protects the drum, so that it always has work flowing to it. Buffers in DBR have time as their unit of measure, rather than quantity of material. This makes the priority system operate strictly based on the time an order is expected to be at the drum. Traditional DBR usually calls for buffers at several points in the system: the constraint, synchronization points and at shipping. S-DBR has a buffer at shipping and manages the flow of work across the drum through a load planning mechanism.
The rope is the work release mechanism for the plant. Orders are released to the shop floor at one “buffer time” before they are due. In other words, if the buffer is 5 days, the order is released 5 days before it is due at the constraint. Putting work into the system earlier than this buffer time is likely to generate too-high work-in-process and slow down the entire system.
The methodology and principles prescribed by ToC discussed above are useful in understanding the invention described herein.